Assignment

1) Data Import & Initial Exploration

The Dataset: telco-customer-churn.csv was used for this assignment. It requires the loading of the dataset and initial exploration of the data. The dataset contains information about customers of a telecommunications company, including their demographics, services subscribed to, and whether they have churned (i.e., stopped using the service).

call library

# if (!require(pacman)) { install.packages("pacman") }

pacman::p_load(tidyverse, bulkreadr, janitor, readxl, writexl)

importing dataset and labelled as telco_data

telco_data <- read_csv("data/telco-customer-churn.csv")

Rows: 7043 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): customerID, gender, Partner, Dependents, PhoneService, MultipleLin...
dbl  (4): SeniorCitizen, tenure, MonthlyCharges, TotalCharges

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Exploring the dataset

head(telco_data)

# A tibble: 6 × 21
  customerID gender SeniorCitizen Partner Dependents tenure PhoneService
  <chr>      <chr>          <dbl> <chr>   <chr>       <dbl> <chr>       
1 7590-VHVEG Female             0 Yes     No              1 No          
2 5575-GNVDE Male               0 No      No             34 Yes         
3 3668-QPYBK Male               0 No      No              2 Yes         
4 7795-CFOCW Male               0 No      No             45 No          
5 9237-HQITU Female             0 No      No              2 Yes         
6 9305-CDSKC Female             0 No      No              8 Yes         
# ℹ 14 more variables: MultipleLines <chr>, InternetService <chr>,
#   OnlineSecurity <chr>, OnlineBackup <chr>, DeviceProtection <chr>,
#   TechSupport <chr>, StreamingTV <chr>, StreamingMovies <chr>,
#   Contract <chr>, PaperlessBilling <chr>, PaymentMethod <chr>,
#   MonthlyCharges <dbl>, TotalCharges <dbl>, Churn <chr>

glimpse(telco_data)

Rows: 7,043
Columns: 21
$ customerID       <chr> "7590-VHVEG", "5575-GNVDE", "3668-QPYBK", "7795-CFOCW…
$ gender           <chr> "Female", "Male", "Male", "Male", "Female", "Female",…
$ SeniorCitizen    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Partner          <chr> "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes…
$ Dependents       <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No"…
$ tenure           <dbl> 1, 34, 2, 45, 2, 8, 22, 10, 28, 62, 13, 16, 58, 49, 2…
$ PhoneService     <chr> "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", …
$ MultipleLines    <chr> "No phone service", "No", "No", "No phone service", "…
$ InternetService  <chr> "DSL", "DSL", "DSL", "DSL", "Fiber optic", "Fiber opt…
$ OnlineSecurity   <chr> "No", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "…
$ OnlineBackup     <chr> "Yes", "No", "Yes", "No", "No", "No", "Yes", "No", "N…
$ DeviceProtection <chr> "No", "Yes", "No", "Yes", "No", "Yes", "No", "No", "Y…
$ TechSupport      <chr> "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes…
$ StreamingTV      <chr> "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Ye…
$ StreamingMovies  <chr> "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes…
$ Contract         <chr> "Month-to-month", "One year", "Month-to-month", "One …
$ PaperlessBilling <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", …
$ PaymentMethod    <chr> "Electronic check", "Mailed check", "Mailed check", "…
$ MonthlyCharges   <dbl> 29.85, 56.95, 53.85, 42.30, 70.70, 99.65, 89.10, 29.7…
$ TotalCharges     <dbl> 29.85, 1889.50, 108.15, 1840.75, 151.65, 820.50, 1949…
$ Churn            <chr> "No", "No", "Yes", "No", "Yes", "Yes", "No", "No", "Y…

summary(telco_data)

     customerID         gender     SeniorCitizen         Partner    
 Length   :7043   Length   :7043   Min.   :0.0000   Length   :7043  
 N.unique :7043   N.unique :   2   1st Qu.:0.0000   N.unique :   2  
 N.blank  :   0   N.blank  :   0   Median :0.0000   N.blank  :   0  
 Min.nchar:  10   Min.nchar:   4   Mean   :0.1621   Min.nchar:   2  
 Max.nchar:  10   Max.nchar:   6   3rd Qu.:0.0000   Max.nchar:   3  
                                   Max.   :1.0000                   
                                                                    
     Dependents       tenure         PhoneService    MultipleLines 
 Length   :7043   Min.   : 0.00   Length   :7043   Length   :7043  
 N.unique :   2   1st Qu.: 9.00   N.unique :   2   N.unique :   3  
 N.blank  :   0   Median :29.00   N.blank  :   0   N.blank  :   0  
 Min.nchar:   2   Mean   :32.37   Min.nchar:   2   Min.nchar:   2  
 Max.nchar:   3   3rd Qu.:55.00   Max.nchar:   3   Max.nchar:  16  
                  Max.   :72.00                                    
                                                                   
  InternetService   OnlineSecurity    OnlineBackup   DeviceProtection
 Length   :7043   Length   :7043   Length   :7043   Length   :7043   
 N.unique :   3   N.unique :   3   N.unique :   3   N.unique :   3   
 N.blank  :   0   N.blank  :   0   N.blank  :   0   N.blank  :   0   
 Min.nchar:   2   Min.nchar:   2   Min.nchar:   2   Min.nchar:   2   
 Max.nchar:  11   Max.nchar:  19   Max.nchar:  19   Max.nchar:  19   
                                                                     
                                                                     
    TechSupport      StreamingTV    StreamingMovies      Contract   
 Length   :7043   Length   :7043   Length   :7043   Length   :7043  
 N.unique :   3   N.unique :   3   N.unique :   3   N.unique :   3  
 N.blank  :   0   N.blank  :   0   N.blank  :   0   N.blank  :   0  
 Min.nchar:   2   Min.nchar:   2   Min.nchar:   2   Min.nchar:   8  
 Max.nchar:  19   Max.nchar:  19   Max.nchar:  19   Max.nchar:  14  
                                                                    
                                                                    
  PaperlessBilling   PaymentMethod  MonthlyCharges    TotalCharges   
 Length   :7043    Length   :7043   Min.   : 18.25   Min.   :  18.8  
 N.unique :   2    N.unique :   4   1st Qu.: 35.50   1st Qu.: 401.4  
 N.blank  :   0    N.blank  :   0   Median : 70.35   Median :1397.5  
 Min.nchar:   2    Min.nchar:  12   Mean   : 64.76   Mean   :2283.3  
 Max.nchar:   3    Max.nchar:  25   3rd Qu.: 89.85   3rd Qu.:3794.7  
                                    Max.   :118.75   Max.   :8684.8  
                                                     NAs    :11      
       Churn     
 Length   :7043  
 N.unique :   2  
 N.blank  :   0  
 Min.nchar:   2  
 Max.nchar:   3

inspect_na(telco_data)

# A tibble: 21 × 3
   col_name          cnt  pcnt
   <chr>           <int> <dbl>
 1 TotalCharges       11 0.156
 2 customerID          0 0    
 3 gender              0 0    
 4 SeniorCitizen       0 0    
 5 Partner             0 0    
 6 Dependents          0 0    
 7 tenure              0 0    
 8 PhoneService        0 0    
 9 MultipleLines       0 0    
10 InternetService     0 0    
# ℹ 11 more rows

2) Data Transformation

Recode the churn column into a binary variable where “Yes” = 1, and “No” = 0

Recode the SeniorCitizen variable into a more descriptive format (e.g. “Yes” → “Senior”, “No” → “non-Senior”)

Create a new column ServiceCount that sums the indicators (1/0) for multiple services (e.g. PhoneService, OnlineSecurity, etc.)

these are the multiple services; PhoneService, MultipleLines, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies

recode churn (“Yes” = 1, and “No” = 0) & recode SeniorCitizen (1 = “Senior”, 0 = “non-senior”)

# telco_data |> mutate(Churn= if_else(Churn=="Yes", 1, 0))
# telco_data |> mutate(SeniorCitizen = if_else(SeniorCitizen == 1, "Senior", "non-senior")) |> View()

telco_data_1 <- telco_data |>
  mutate(
    Churn = if_else(Churn == "Yes", 1, 0),
    SeniorCitizen = if_else(SeniorCitizen == 1, "Senior", "non-senior")
  )
names(telco_data_1)

 [1] "customerID"       "gender"           "SeniorCitizen"    "Partner"         
 [5] "Dependents"       "tenure"           "PhoneService"     "MultipleLines"   
 [9] "InternetService"  "OnlineSecurity"   "OnlineBackup"     "DeviceProtection"
[13] "TechSupport"      "StreamingTV"      "StreamingMovies"  "Contract"        
[17] "PaperlessBilling" "PaymentMethod"    "MonthlyCharges"   "TotalCharges"    
[21] "Churn"

converting Yes/No to 1/0 for the multiple services

telco_data_2 <- telco_data_1 |> 
  mutate(
    PhoneService = if_else(PhoneService=="Yes", 1, 0), 
    OnlineSecurity = if_else(OnlineSecurity=="Yes", 1, 0), 
    OnlineBackup = if_else(OnlineBackup == "Yes", 1, 0), 
    DeviceProtection = if_else(DeviceProtection== "Yes", 1, 0), 
    TechSupport = if_else(TechSupport== "Yes", 1, 0), 
    StreamingTV = if_else(StreamingTV== "Yes", 1, 0), 
    StreamingMovies = if_else(StreamingMovies== "Yes", 1, 0)
  )

create a new column ServiceCount

telco_data_clean <- telco_data_2 |> 
  mutate(ServiceCount = PhoneService + OnlineSecurity +  OnlineBackup + DeviceProtection + TechSupport + StreamingTV + StreamingMovies) 

glimpse(telco_data_clean)

Rows: 7,043
Columns: 22
$ customerID       <chr> "7590-VHVEG", "5575-GNVDE", "3668-QPYBK", "7795-CFOCW…
$ gender           <chr> "Female", "Male", "Male", "Male", "Female", "Female",…
$ SeniorCitizen    <chr> "non-senior", "non-senior", "non-senior", "non-senior…
$ Partner          <chr> "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes…
$ Dependents       <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No"…
$ tenure           <dbl> 1, 34, 2, 45, 2, 8, 22, 10, 28, 62, 13, 16, 58, 49, 2…
$ PhoneService     <dbl> 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ MultipleLines    <chr> "No phone service", "No", "No", "No phone service", "…
$ InternetService  <chr> "DSL", "DSL", "DSL", "DSL", "Fiber optic", "Fiber opt…
$ OnlineSecurity   <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1,…
$ OnlineBackup     <dbl> 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,…
$ DeviceProtection <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1,…
$ TechSupport      <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,…
$ StreamingTV      <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1,…
$ StreamingMovies  <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1,…
$ Contract         <chr> "Month-to-month", "One year", "Month-to-month", "One …
$ PaperlessBilling <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", …
$ PaymentMethod    <chr> "Electronic check", "Mailed check", "Mailed check", "…
$ MonthlyCharges   <dbl> 29.85, 56.95, 53.85, 42.30, 70.70, 99.65, 89.10, 29.7…
$ TotalCharges     <dbl> 29.85, 1889.50, 108.15, 1840.75, 151.65, 820.50, 1949…
$ Churn            <dbl> 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ ServiceCount     <dbl> 1, 3, 3, 3, 1, 4, 3, 1, 5, 3, 2, 1, 4, 5, 6, 7, 1, 5,…

summary(telco_data_clean)

     customerID         gender       SeniorCitizen       Partner    
 Length   :7043   Length   :7043   Length   :7043   Length   :7043  
 N.unique :7043   N.unique :   2   N.unique :   2   N.unique :   2  
 N.blank  :   0   N.blank  :   0   N.blank  :   0   N.blank  :   0  
 Min.nchar:  10   Min.nchar:   4   Min.nchar:   6   Min.nchar:   2  
 Max.nchar:  10   Max.nchar:   6   Max.nchar:  10   Max.nchar:   3  
                                                                    
                                                                    
     Dependents       tenure       PhoneService      MultipleLines 
 Length   :7043   Min.   : 0.00   Min.   :0.0000   Length   :7043  
 N.unique :   2   1st Qu.: 9.00   1st Qu.:1.0000   N.unique :   3  
 N.blank  :   0   Median :29.00   Median :1.0000   N.blank  :   0  
 Min.nchar:   2   Mean   :32.37   Mean   :0.9032   Min.nchar:   2  
 Max.nchar:   3   3rd Qu.:55.00   3rd Qu.:1.0000   Max.nchar:  16  
                  Max.   :72.00   Max.   :1.0000                   
                                                                   
  InternetService OnlineSecurity    OnlineBackup    DeviceProtection
 Length   :7043   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 N.unique :   3   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
 N.blank  :   0   Median :0.0000   Median :0.0000   Median :0.0000  
 Min.nchar:   2   Mean   :0.2867   Mean   :0.3449   Mean   :0.3439  
 Max.nchar:  11   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
                  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
                                                                    
  TechSupport      StreamingTV     StreamingMovies       Contract   
 Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length   :7043  
 1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   N.unique :   3  
 Median :0.0000   Median :0.0000   Median :0.0000   N.blank  :   0  
 Mean   :0.2902   Mean   :0.3844   Mean   :0.3879   Min.nchar:   8  
 3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000   Max.nchar:  14  
 Max.   :1.0000   Max.   :1.0000   Max.   :1.0000                   
                                                                    
  PaperlessBilling   PaymentMethod  MonthlyCharges    TotalCharges   
 Length   :7043    Length   :7043   Min.   : 18.25   Min.   :  18.8  
 N.unique :   2    N.unique :   4   1st Qu.: 35.50   1st Qu.: 401.4  
 N.blank  :   0    N.blank  :   0   Median : 70.35   Median :1397.5  
 Min.nchar:   2    Min.nchar:  12   Mean   : 64.76   Mean   :2283.3  
 Max.nchar:   3    Max.nchar:  25   3rd Qu.: 89.85   3rd Qu.:3794.7  
                                    Max.   :118.75   Max.   :8684.8  
                                                     NAs    :11      
     Churn         ServiceCount  
 Min.   :0.0000   Min.   :0.000  
 1st Qu.:0.0000   1st Qu.:1.000  
 Median :0.0000   Median :3.000  
 Mean   :0.2654   Mean   :2.941  
 3rd Qu.:1.0000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :7.000

GBG Version

telco_data_2. <- 
  telco_data_1 |> 
   mutate(
     ServiceCount = 
       (PhoneService == "Yes") +
       (OnlineSecurity == "Yes") +
       (OnlineBackup == "Yes") +
       (DeviceProtection == "Yes") +
       (TechSupport == "Yes") +
       (StreamingTV == "Yes") +
       (StreamingMovies == "Yes"
        )
   )
telco_data_2.

# A tibble: 7,043 × 22
   customerID gender SeniorCitizen Partner Dependents tenure PhoneService
   <chr>      <chr>  <chr>         <chr>   <chr>       <dbl> <chr>       
 1 7590-VHVEG Female non-senior    Yes     No              1 No          
 2 5575-GNVDE Male   non-senior    No      No             34 Yes         
 3 3668-QPYBK Male   non-senior    No      No              2 Yes         
 4 7795-CFOCW Male   non-senior    No      No             45 No          
 5 9237-HQITU Female non-senior    No      No              2 Yes         
 6 9305-CDSKC Female non-senior    No      No              8 Yes         
 7 1452-KIOVK Male   non-senior    No      Yes            22 Yes         
 8 6713-OKOMC Female non-senior    No      No             10 No          
 9 7892-POOKP Female non-senior    Yes     No             28 Yes         
10 6388-TABGU Male   non-senior    No      Yes            62 Yes         
# ℹ 7,033 more rows
# ℹ 15 more variables: MultipleLines <chr>, InternetService <chr>,
#   OnlineSecurity <chr>, OnlineBackup <chr>, DeviceProtection <chr>,
#   TechSupport <chr>, StreamingTV <chr>, StreamingMovies <chr>,
#   Contract <chr>, PaperlessBilling <chr>, PaymentMethod <chr>,
#   MonthlyCharges <dbl>, TotalCharges <dbl>, Churn <dbl>, ServiceCount <int>

3) Grouping & Summarisation

Use group_by() and summarise() to calculate the churn rate for key segments (e.g. by Contract, InternetService)

churn rate by contract

telco_by_contract <- telco_data_clean |>
  group_by(Contract) |>
  summarise(
    churn = sum(Churn, na.rm = TRUE),
    churn_rate = mean(Churn) * 100
  )
telco_by_contract

# A tibble: 3 × 3
  Contract       churn churn_rate
  <chr>          <dbl>      <dbl>
1 Month-to-month  1655      42.7 
2 One year         166      11.3 
3 Two year          48       2.83

churn rate by Internet Service

telco_by_inter <- telco_data_clean |>
  group_by(InternetService) |>
  summarise(
    churn = sum(Churn),
    churn_rate = mean(Churn) * 100
  )

churn rate by both contract and Internet Service

telco_by_contract_internet <- telco_data_clean |> group_by(Contract, InternetService) |> 
  summarise(churn = sum(Churn, na.rm = TRUE), 
            churn_rate = mean(Churn)*100)

`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by Contract and InternetService.
ℹ Output is grouped by Contract.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(Contract, InternetService))` for per-operation
  grouping (`?dplyr::dplyr_by`) instead.

telco_by_contract_internet

# A tibble: 9 × 4
# Groups:   Contract [3]
  Contract       InternetService churn churn_rate
  <chr>          <chr>           <dbl>      <dbl>
1 Month-to-month DSL               394     32.2  
2 Month-to-month Fiber optic      1162     54.6  
3 Month-to-month No                 99     18.9  
4 One year       DSL                53      9.30 
5 One year       Fiber optic       104     19.3  
6 One year       No                  9      2.47 
7 Two year       DSL                12      1.91 
8 Two year       Fiber optic        31      7.23 
9 Two year       No                  5      0.784

Calculate the overall churn rate

overall_churn <- telco_data_clean |> 
  summarise(total_churn = sum(Churn), 
            churn_rate = mean(Churn)*100)
overall_churn

# A tibble: 1 × 2
  total_churn churn_rate
        <dbl>      <dbl>
1        1869       26.5

churn rate for each Contract type

contract_churn <- telco_data_clean |> 
  group_by(Contract) |> 
  summarise(count = n(), 
            churn = sum(Churn), 
            churn_rate = mean(Churn)*100)
contract_churn

# A tibble: 3 × 4
  Contract       count churn churn_rate
  <chr>          <int> <dbl>      <dbl>
1 Month-to-month  3875  1655      42.7 
2 One year        1473   166      11.3 
3 Two year        1695    48       2.83

Summary statistics for internetservices (count, Average monthly charges, Average tenure)

summary_internet <- telco_data_clean |> group_by(InternetService) |> 
  summarise( ave_monthly_charges = mean(MonthlyCharges, 
                                        na.rm = TRUE),
             ave_tenure = mean(tenure, na.rm = TRUE))
summary_internet

# A tibble: 3 × 3
  InternetService ave_monthly_charges ave_tenure
  <chr>                         <dbl>      <dbl>
1 DSL                            58.1       32.8
2 Fiber optic                    91.5       32.9
3 No                             21.1       30.5

Use across() to summarise monthly charges for both genders in one concise step

# by use of summarise(.by)
gender_summary <- telco_data_clean |>
  summarise(
    across(
      MonthlyCharges,
      list(
        Mean = ~ mean(.x, na.rm = TRUE),
        Median = ~ median(.x, na.rm = TRUE),
        Minimum = ~ min(.x, na.rm = TRUE),
        Maximum = ~ max(.x, na.rm = TRUE)
      )
    ),
    .by = gender
  )
gender_summary

# A tibble: 2 × 5
  gender MonthlyCharges_Mean MonthlyCharges_Median MonthlyCharges_Minimum
  <chr>                <dbl>                 <dbl>                  <dbl>
1 Female                65.2                  70.6                   18.4
2 Male                  64.3                  70.1                   18.2
# ℹ 1 more variable: MonthlyCharges_Maximum <dbl>

# by use of group_by() and summarise() combo
telco_data_clean |>
  group_by(gender) |>
  summarise(
    mean_charges = mean(MonthlyCharges, na.rm = TRUE),
    median_charges = median(MonthlyCharges, na.rm = TRUE),
    min_charges = min(MonthlyCharges, na.rm = TRUE),
    max_charges = max(MonthlyCharges, na.rm = TRUE)
  )

# A tibble: 2 × 5
  gender mean_charges median_charges min_charges max_charges
  <chr>         <dbl>          <dbl>       <dbl>       <dbl>
1 Female         65.2           70.6        18.4        119.
2 Male           64.3           70.1        18.2        118.

telco_data_clean

# A tibble: 7,043 × 22
   customerID gender SeniorCitizen Partner Dependents tenure PhoneService
   <chr>      <chr>  <chr>         <chr>   <chr>       <dbl>        <dbl>
 1 7590-VHVEG Female non-senior    Yes     No              1            0
 2 5575-GNVDE Male   non-senior    No      No             34            1
 3 3668-QPYBK Male   non-senior    No      No              2            1
 4 7795-CFOCW Male   non-senior    No      No             45            0
 5 9237-HQITU Female non-senior    No      No              2            1
 6 9305-CDSKC Female non-senior    No      No              8            1
 7 1452-KIOVK Male   non-senior    No      Yes            22            1
 8 6713-OKOMC Female non-senior    No      No             10            0
 9 7892-POOKP Female non-senior    Yes     No             28            1
10 6388-TABGU Male   non-senior    No      Yes            62            1
# ℹ 7,033 more rows
# ℹ 15 more variables: MultipleLines <chr>, InternetService <chr>,
#   OnlineSecurity <dbl>, OnlineBackup <dbl>, DeviceProtection <dbl>,
#   TechSupport <dbl>, StreamingTV <dbl>, StreamingMovies <dbl>,
#   Contract <chr>, PaperlessBilling <chr>, PaymentMethod <chr>,
#   MonthlyCharges <dbl>, TotalCharges <dbl>, Churn <dbl>, ServiceCount <dbl>

4. Data Visualization

Exploratory visualizations

Distribution of customer tenure

# tenure-histogram


ggplot(telco_data_clean, aes(x = tenure)) +
  geom_histogram(binwidth = 5, boundary = 0, color = "white") +
  labs(x = "Tenure (months)", y = "Number of customers") +
  theme_minimal()

The histogram reveals how customers are distributed across the customer lifecycle and helps identify concentrations of very new and long-standing customers.

Churned versus non-churned customers

# churn-bar


ggplot(telco_data_clean, aes(x = Churn, fill = Churn)) +
  geom_bar(show.legend = FALSE) +
  labs(x = "Churn status", y = "Number of customers") +
  theme_minimal()

Warning: The following aesthetics were dropped during statistical transformation: fill.
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

The bar chart makes the class balance visible and provides context for interpreting the overall churn rate.

Discussion Points

Data transformation with dplyr converts raw records into analysis-ready variables and summaries. In this report, mutate() creates the binary churn indicator, descriptive senior-citizen label, average charge metric, and service count. group_by() and summarise() produce churn rates and segment-level statistics, while across() applies the same statistical functions efficiently to multiple variables. The result is shorter, more readable, reproducible code and a dataset that can move naturally from exploratory analysis into predictive modelling.